A large body of research has shown ethnic diversity to have a negative impact on development. This column suggests that it is the unequal concentration of wealth across ethnic lines that is detrimental for development rather than diversity per se. It shows that ethnic inequality, measured using ethno-linguistic maps and satellite images of light density at night, is associated with lower GDP per capita, worse living conditions, and lower levels of education.

Rising inequality is increasingly a concern – spurred on by the Occupy Wall Street movement and the recent evidence. The idea that inequality spurs conflict leading to under-development dates back at least to Thomas Hobbes and Karl Marx. Yet, perhaps due to measurement-data issues and theoretical ambiguities, a vast body of research has failed to detect significant associations between income inequality and development or conflict. See, for example, the reviews of Benabou (2005) and Galor (2011).

At the same time, the resurgence of violence and conflict in Syria, Mali, and Egypt and the obstacles that many countries of the Arab Spring face in consolidating democracy have led influential commentators to emphasise the difficulties associated with ethnic (and often religious) fractionalisation. A large body of research in economics, sociology, and political science has examined the role of ethnic-linguistic diversity in shaping comparative development finding some evidence supportive of the conventional wisdom. See Alesina and La Ferrara (2005) for a review.

New research

In recent work (Alesina et al 2012) we put forward and test an alternative conjecture that stresses the intersection of economic inequality and ethnic diversity, namely ethnic inequality.

Our thesis is that inequality is especially deleterious when wealth is unevenly distributed across ethnic or religious lines.

First, inequality in income along ethnic lines is likely to exacerbate the salience of group identity, limit social cohesion by increasing between-group animosity, impede institutional development, lead to state capture, and spur conflict.

Second, income differences across ethnic groups are often both the cause and the consequence of discriminatory policies including the unequal provision of public goods across groups.

Third, as Chua (2003) discusses, in several parts of the world a small (usually market-dominant) ethnic minority controls a sizeable portion of the economy and exerts disproportionate political influence. She lists as examples the Kikuyu in Kenya, the I(g)bo in Nigeria, white minorities in South Africa, Lebanese groups in many parts of western Africa, Chinese minorities in the Philippines and other east Asian countries, small Christian communities in many Arab countries.

The presence of an economically dominant ethnic minority may lower support for free-market institutions (such as trade liberalisation, privatisation, and financial openness), as the majority of the population usually feels that the benefits of capitalism go to just a handful of ethnic groups.

New data on ethnic inequality

The first contribution of our paper is to provide measures of within-country differences in wellbeing across ethnic groups, coined as 'ethnic inequality'. Information on income levels of ethnic groups for all countries is not available. Hence, to construct country-level indicators of ethnic inequality (Gini coefficients) for the largest possible sample, we combine ethno-linguistic maps on the location of ethnic groups with satellite images of light density at night, which are available at a fine grid and recent works show that are good proxies of development (see Henderson et al. 2012 and Michalopoulos and Papaioannou 2012, 2013).To isolate the cross-ethnic component of inequality from the overall degree of inequality across regions we also construct proxies of spatial inequality.

Figures 1a and 1b illustrate the cross-country distribution of ethnic and spatial inequality. Africa and south Asia are the most ethnically unequal places in the world. In contrast, western Europe is the region with the lowest level of ethnic inequality.

Ethnic inequality is naturally correlated with the overall degree of spatial inequality. Since we are interested in uncovering the role of ethnic inequality beyond the overall spatial inequality Figure 2 portrays the global distribution of ethnic inequality partialling out the effect of the overall degree of spatial inequality. A few interesting patterns emerge. On the one hand, Sudan, Afghanistan, and Mongolia have much higher ethnic inequality as compared to the overall degree of spatial inequality (which is also very high). On the other hand, the US and Canada score low in ethnic inequality as compared to the overall degree of spatial inequality (which is quite high). Azerbaijan, Syria, Albania, Tunisia, Haiti, and Rwanda score quite high in ethnic inequality, while in contrast the overall degree of spatial inequality is very low1.

Ethnic inequality and development

We then relate the newly constructed measures of ethnic inequality to economic development. Figures 3a and 3b illustrate the remarkably strong negative association between ethnic inequality and (the natural logarithm of) real GDP per capita in 2000 (the results are very similar for 1992 and 2010).

This correlation holds when we account for continental differences and most importantly when we condition on the overall degree of spatial inequality, that is, spatial (non-ethnicity-specific) differences in economic activity. The latter is also inversely related to economic wellbeing. Our estimates imply that a reduction in the ethnic Gini coefficient by 0.25 (approximately one standard deviation, from the level of Nigeria where the ethnic Gini is 0.76 to the level of Namibia where the ethnic Gini is 0.50) is associated by 31% increase in per capita GDP.

We also document that the negative correlation between ethno-linguistic fragmentation and development documented in previous works (Easterly and Levine 1997 and Alesina et al. 2003) weakens considerably when we account for ethnic inequality; this suggests that it is the unequal concentration of wealth across ethnic lines that is detrimental for development rather than diversity per se.

A primer on the origins of ethnic inequality

The correlation between ethnic inequality and economic development does not necessarily reflect a causal relationship. In order to gain some insight on the directionality of the uncovered relationship we explore the factors shaping contemporary differences in wellbeing across ethnicities.

Motivated by the findings of Michalopoulos (2012) that ethno-linguistic diversity increases with geographic heterogeneity, we conjecture that geographic and ecological endowments play a role in explaining contemporary ethnic inequality. To test this conjecture, we construct composite indicators of inequality in geographic endowments across ethnic historical homelands using data on a variety of geographic characteristics including a group’s homeland land quality for agriculture, elevation, access to the coast, and presence of water bodies; and then we associate inequality in geographic endowments across ethnic homelands with contemporary inequality in development across ethnic regions.

Our analysis shows that income differences across ethnic homelands captured by differences in luminosity per capita have a significant geographic component. Figures 4a and 4b illustrate the strong positive cross-country correlation between a composite index of inequality in geographic endowments across ethnic homelands and ethnic inequality (as reflected in lights per capita).

Micro evidence from sub-Saharan Africa

The interpretation of cross-country regressions is always challenging as countries differ across many and hard-to-account-for dimensions. In an effort to improve on the cross-country evidence, we used individual-level data from 17 sub-Saharan countries on wellbeing, education, public goods provision, and ethnic identification and explored whether ethnic inequality correlates with various development proxies across districts in the same country. Moreover, since members of the same ethnic group are present in more than one district, we can look within members of the same ethnic group.

Exploiting information from more than 20,000 respondents, our analysis shows that, conditional on an array of individual characteristics, respondents living in the same country and coming from the same ethnic group report worse living conditions, lower levels of education, and inadequate access to basic public goods when they reside in districts/regions characterised with a higher degree of ethnic inequality. These results thus suggest that ethnic inequality may be an important, and so far neglected, feature of African underdevelopment.

Conclusion

Our conjecture has been that what is harmful for development is neither inequality per se nor ethnic (or religious) fractionalisation, but the combination of the two. When wealth and poverty are clearly associated with distinct ethnic groups, the resulting ethnic inequality hampers development by generating hatred, social immobility, envy, sense of unfairness, and conflict, which create obstacles to the smooth functioning on the polity and of the economic system. Disentangling the relative importance of these channels is a fruitful avenue of future research.

Galor, Oded. (2011): “Inequality, Human Capital and the Process of Development,” in The Handbook of the Economics of Education, ed. by S M Eric A Hanushek, and Leonard Woessmann. Elsevier, Amsterdam, Netherlands.

1 The cross-ethnic group inequality index is weakly correlated with the commonly employed -and notoriously poorly measured- income inequality measures at the country level. Moreover, there is a weak-to-moderate association between the newly constructed measures of ethnic inequality and indicators reflecting ethno-linguistic fractionalisation. This implies that the new measures capture a distinct aspect of ethnic fragmentation and inequality.